CN105139670A - Video-based regional self-optimizing signal control method and apparatus - Google Patents
Video-based regional self-optimizing signal control method and apparatus Download PDFInfo
- Publication number
- CN105139670A CN105139670A CN201510443091.0A CN201510443091A CN105139670A CN 105139670 A CN105139670 A CN 105139670A CN 201510443091 A CN201510443091 A CN 201510443091A CN 105139670 A CN105139670 A CN 105139670A
- Authority
- CN
- China
- Prior art keywords
- intersection
- traffic
- regional
- mrow
- road network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000001514 detection method Methods 0.000 claims abstract description 27
- 238000005457 optimization Methods 0.000 claims abstract description 20
- 238000004891 communication Methods 0.000 claims abstract description 7
- 238000013500 data storage Methods 0.000 claims description 9
- 238000004220 aggregation Methods 0.000 claims description 4
- 230000002776 aggregation Effects 0.000 claims description 4
- 238000009826 distribution Methods 0.000 claims description 3
- 230000001939 inductive effect Effects 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 3
- 101001003151 Caretta caretta Chelonianin Proteins 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 4
- 238000009434 installation Methods 0.000 description 4
- 238000007726 management method Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000006698 induction Effects 0.000 description 3
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006116 polymerization reaction Methods 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention provides a video-based regional self-optimizing signal control method, and mainly relates to the regional road network intersection signal optimization control field. Through comprehensive applications of a novel video vehicle detection device, dynamic detection of traffic states of multiple intersections of a regional road network as well as signal optimization can be achieved. According to the technical scheme, the method includes the steps: installing video equipment; performing data acquisition and communication; processing and calculating regional road network self-optimizing signals; and issuing and controlling signal instructions. The invention also provides a video-based regional self-optimizing signal control apparatus. Through adoption of an active video technology, the real-time traffic states of the intersections of the regional road network can be accurately detected, a regional optimized signal control scheme can be made, real-time decision and emergency handling information can be provided to traffic management and control, and the operating efficiency and the service level of regional road network traffic can be improved.
Description
Technical Field
The invention relates to the field of traffic signal optimization control of a plurality of intersections of a regional road network, in particular to a regional self-optimization signal control method and a device based on video.
Background
The urban traffic jam and accidents frequently occur day by day, particularly, the traffic management system with serious, advanced and applicable road jam events at intersections is one of the most effective ways for solving the urban traffic jam, the traffic signal control is the core of the traffic management system, and the optimal control of the traffic signals of the regional road network can furthest exert the regional traffic induction advantages and improve the road traffic operation efficiency.
The video vehicle detection technology is a technology that video acquisition equipment is installed on a road with complex conditions or easy congestion, the number, speed and queuing length of passing vehicles are detected, acquired data are transmitted back to a server center through a wired or wireless network to be processed, dynamic traffic signal control can be performed through real-time acquired traffic parameters, effective rule induction of traffic flow is achieved, and traffic congestion is reduced to the maximum extent.
At present, a signal control method mainly comprises timing control, multi-period control, induction control, self-adaptive control and the like, and the traditional model algorithm is too hard to set a threshold value according to the change of a certain traffic parameter to carry out signal optimization, so that the misjudgment of the system on the state can be caused; the invention provides a video-based regional self-optimization signal control method, which extracts a signal control self-optimization algorithm of a plurality of intersections of a regional road network through real-time detection and comprehensive analysis of intersection running indexes, and can greatly improve the running efficiency of regional road network road traffic.
Disclosure of Invention
A video-based regional self-optimization signal control method and a device thereof are provided, the device used in the method comprises a video device, a data communication device, a data storage and standardization server, a regional road network self-optimization signal processing server and a signal distribution terminal device, all the devices are connected according to the sequence signal, the method comprises the following steps:
(1) installing video vehicle detection equipment in each entrance direction of the intersection, adjusting the angle of a detection surface, and determining that a detection area and a blind area critical line are located 10-20 meters before a stop line;
(2) numbering intersections of a regional road network in sequence, numbering detectors of each intersection in a clockwise direction, and binding road section numbers to which the intersections belong with the detector numbers;
(3) collecting traffic flow and vehicle speed parameter information of a detection section in real time through video vehicle detection equipment, wherein the parameter information is transmitted back to a data storage and standardization server in real time through data communication equipment to carry out real-time data storage and standardization processing;
(4) extracting real-time traffic data of a storage server, calculating an average traffic flow density parameter of a detection section, and calculating a real-time road section traffic operation index according to the average traffic flow density;
(5) according to the traffic operation indexes of all road sections in the entrance direction of the intersection and the road grade attributes of the road sections, the traffic operation indexes of the intersection are calculated in an aggregation mode, the signal control period of the intersection is calculated through an intersection traffic operation index-signal period relation model, and then the signal period of each intersection is obtained;
(6) calculating a traffic operation index of a main line of the intersection according to the traffic operation index of each road section in the inlet direction of the intersection and the road grade attribute of the road section, and comprehensively calculating the average value of the traffic operation indexes of the intersection of the regional road network;
(7) calculating the green ratio of each intersection trunk line of the regional road network according to a trunk line priority principle based on a regional road network intersection traffic running index average value-green ratio model, and then calculating the time of each intersection trunk line signal control red light and green light;
(8) and sending real-time parameters of intersection signal control to the signal control lamp by using the signal issuing terminal equipment and calling a database interface service, and dynamically inducing traffic of each intersection of the regional road network through the signal control lamp.
A video-based regional self-optimization signal control method and device are characterized in that: constructing a traffic operation index model based on a video, constructing a traffic operation index-signal period relation model, and constructing a regional road network intersection traffic operation index average value-green ratio model;
the video-based traffic operation index model is constructed by 4 parts of a road section traffic operation index, an intersection trunk traffic operation index and an average value of regional road network traffic operation indexes;
(A) extracting traffic flow data and speed data of each lane of a video detection section, and respectively calculating an average traffic flow parameter and an average speed parameter of the detection section on the level of space dimension and time dimension;
average traffic flow parameterBy the formulaCalculating to obtain the data, wherein N is the lane where the vehicle is located, N is the total number of lanes of the road section, and q is the total number of lanes of the road sectionnA traffic flow for an nth lane; average velocity parameter vnBy passingIs calculated to obtain, wherein vnIs the speed of the n-th lane,average speed per granularity period;
(B) average traffic flow density parameterBy the formulaCalculating to obtain;
(C) road section traffic operation index RTPI passing formula
(D) intersection traffic operation index ITPI passing formula
ITPI＝RTPI1*ω1+RTPI2*ω2+,...,+RTPIj*ωjIs calculated to obtain, wherein ω1,ω2,...,ωjWeighting coefficients for each inlet direction;
(E) intersection main line traffic operation index IATPI passing bulletin
IATPI＝RTPI1*ω1+RTPI2*ω2+,...,+RTPIh*ωhIs calculated to obtain, wherein ω1,ω2,...,ωhWeighting coefficients for the crossroad trunk sections;
constructing a traffic operation index-signal period relation model, wherein a signal period parameter C is T and ITPI/10, and T is a preset signal period parameter;
the regional road network intersection traffic running index average value-split green ratio model is constructed, and the regional road network intersection traffic running index average value AITPImeanIntersection trunk line split parameterGreen time G of main line at intersectioni＝Ci×riRed light time R of main line of intersectioni＝Ci-Gi-Y，CiControlling the cycle time for the signal at the ith intersection, GiThe green time, R, of the road network of the ith intersection areaiThe red light time of the road network of the ith intersection area is shown, and the Y represents the yellow light time.
By adopting the active video technology, the invention can accurately detect the real-time traffic states of a plurality of intersections of the regional road network, formulate the optimal signal control scheme of the regional road network, provide real-time decision and emergency processing information for traffic management and control and improve the operation efficiency and service level of the regional road network traffic.
Drawings
FIG. 1 is a flow chart of the operation of the present invention;
FIG. 2 is a schematic diagram of the system installation used in FIG. 1;
fig. 3 is a schematic diagram of system device connections used in fig. 1.
Detailed Description
As shown in fig. 1 and 2, a video-based regional self-optimization signal control method and device, the method uses devices including a video vehicle detection device 1, a data communication device 2, a data storage and standardization server 3, a regional network self-optimization processing server 4 and a distribution terminal device 5, the devices are connected in sequence by signals, the method includes the following steps:
s1, installing video vehicle detection equipment in each entrance direction of the intersection, adjusting the angle of a detection surface, and determining that a detection area and a blind area critical line are located 10-20 meters before a stop line;
s2, numbering intersections of the regional road network in sequence, numbering detectors of each intersection in the clockwise direction, and binding the road section number to which the intersection belongs with the detector number;
s21, numbering the intersections on the regional road network in sequence, wherein the number of the intersection is IiI is an intersection ordering label, I is the total number of intersections on the regional road network, and I is less than or equal to I;
s22, the types of the intersections are various, five-way intersections, crossroads and T-shaped intersections are common, and the method classifies the intersections according to the number (J) of the intersection inlet directions.
S23, aiming at each intersection IiNumbering the detectors in a clockwise direction, the detectors being numberedNumber DijI is the number of the intersection, J is the number of the inlet direction, and J is less than or equal to J;
s24, the range of the road section which can be detected by the video is 10-100 meters, the blind area is within 10 meters before the installation position, and the dynamic information of the vehicle cannot be detected in the blind area, so the installation position of the intersection video equipment is very important, after the road section to be detected is determined, the detection area and the blind area critical line are located 10-20 meters in front of the stop line, and the equipment installation schematic diagram of a common intersection is shown in figure 2.
S3, collecting traffic flow and vehicle speed parameter information of a detection section in real time through video vehicle detection equipment, and transmitting the parameter information back to a data storage and standardization server in real time through data communication equipment for real-time data storage and standardization processing;
s4, extracting real-time traffic data of the storage server, calculating average traffic flow density parameters of the detection sections, and calculating a real-time road section traffic operation index according to the average traffic flow density;
s41 average traffic flow density
The data format reported by the video equipment in real time is (t, n, q, v), t represents the reporting time, n represents the lane, q represents the traffic flow data, v represents the vehicle flow speed data, and the units of (t, n, q, v) are respectively second, 1, vehicle/hour/lane and kilometer/hour.
Assume that a sample data set may be represented as S { (t,1, q)1,v1),(t,2,q2,v2),...,(t,n,qn,vn) And counting the average traffic flow of the space dimension and the time dimension of the road section to be measured in timeAverage speed per unit grain size periodAverage traffic density of space dimension and time dimension of road section to be measured(unit: vehicle/km/lane), then
In the above formula: n is the lane; n is the total number of lanes of the road section; q. q.snA traffic flow for an nth lane; v. ofnIs the speed of the nth lane.
S42 road section traffic operation index
Constructing a road section traffic operation index RTPI (roadtrafficPerformanceIndex) and an average traffic flow densityThe functional relationship model of (a) is,
the x, y, z, p and m values are road traffic congestion feeling optimization parameters, questionnaires and data analysis fitting calculation are needed, different road grades and different parameter sizes are needed, and the initial reference values of the system are suggested as shown in table 1.
TABLE 1 road segment traffic operation index model parameters
S5, according to the traffic operation indexes of all the road sections in the entrance direction of the intersection and the road grade attributes of the road sections, calculating the traffic operation indexes of the intersection in an aggregation manner, and calculating the signal control period of the intersection through an intersection traffic operation index-signal period relation model so as to obtain the signal period of each intersection;
s51 intersection running index
The intersection operational index ITPI (intersectiontrafficPerformance index) is a polymerization analysis calculation based on the traffic operational index of each section of the intersection in the inlet direction,
ITPI＝RTPI1*ω1+RTPI2*ω2+,...,+RTPIj*ωj(5)
ω1,ω2,...,ωjweighting coefficients for each inlet direction;
RTPIjcalculating a road section traffic operation index for each inlet road section detector of the intersection;
the weighting coefficients for the intersection entry direction are related to road class, see table 2:
TABLE 2 road grade and intersection weight relationship table
| Road grade | Express way | Main road | Secondary trunk road | Branch circuit |
| Weighted value | P1 | P2 | P3 | P4 |
The weight value calculation formula of a certain inlet direction at the intersection is as follows:
wherein:
ωj' is a weight value corresponding to the grade of the road in the inlet direction;
j is the total number of the inlet directions of the intersection, J is the inlet direction number, and J is less than or equal to J;
s52 intersection signal control period
According to the traffic operation indexes of all road sections in the entrance direction of the intersection and the road grade attributes of the road sections, the traffic operation indexes of the intersection are calculated in an aggregation mode, and the signal control period of the intersection is calculated through an intersection traffic operation index-signal period relation model;
C＝T*ITPI/10(7)
wherein,
c is the signal control cycle time;
t is a preset signal period parameter;
s6, calculating an intersection main line traffic operation index according to the traffic operation index of each road section in the entrance direction of the intersection and the road grade attribute of the road section, and comprehensively calculating the average value of the intersection traffic operation indexes of the regional road network;
s61 intersection main line traffic operation index IATPI
IATPI＝RTPI1*ω1+RTPI2*ω2+,...,+RTPIh*ωh(8)
ω1,ω2,...,ωhWeighting coefficients for the crossroad trunk sections;
s62, area road network intersection traffic running index average value AITPImean
ITPIiOf the ith intersectionAn intersection traffic movement index;
i is the total number of intersections on the regional road network;
s7, calculating the split ratio r of each intersection trunk line of the regional road network based on the regional road network intersection traffic running index average value-split ratio modeliThen calculating the main line signal of each intersection to control the green light GiAnd red light time Ri；
Gi＝Ci×ri(11)
Ri＝Ci-Gi-Y(12)
Wherein,
Cicontrolling the cycle time for the signal of the ith intersection;
Githe time of green light of the road network of the ith intersection area is set;
Rithe red light time of the road network of the ith intersection area;
y represents yellow light time.
And S8, entering the release terminal 5, calling the database interface service, sending the signal control real-time parameters in the equipment 4 to the signal control lamp, and dynamically inducing intersection traffic through the signal control lamp.
The invention fully utilizes the traffic flow and vehicle speed parameters of the video information acquisition equipment to carry out data mining analysis, constructs a road section traffic operation index model and an intersection operation index model based on video, realizes the self-optimization control of regional road network intersection signals, provides real-time decision and emergency data for traffic management and control, reduces traffic accidents, can increase the intersection operation efficiency and improves the service level of regional road network traffic.
It will be appreciated by those skilled in the art that the above embodiments are illustrative only and not intended to be limiting, and that suitable modifications and variations may be made to the above embodiments without departing from the true spirit and scope of the invention.
Claims (5)
1. A video-based regional self-optimization signal control method and device, the device used in the method includes video device, data communication device, data storage and standardization server, regional self-optimization signal processing server and signal distribution terminal device, the devices are connected according to the order signal, characterized in that: the method comprises the following steps:
(1) installing video vehicle detection equipment in each entrance direction of the intersection, adjusting the angle of a detection surface, and determining that a detection area and a blind area critical line are located 10-20 meters before a stop line;
(2) numbering intersections of a regional road network in sequence, numbering detectors of each intersection in a clockwise direction, and binding road section numbers to which the intersections belong with the detector numbers;
(3) collecting traffic flow and vehicle speed parameter information of a detection section in real time through video vehicle detection equipment, wherein the parameter information is transmitted back to a data storage and standardization server in real time through data communication equipment to carry out real-time data storage and standardization processing;
(4) extracting real-time traffic data of a storage server, calculating an average traffic flow density parameter of a detection section, and calculating a real-time road section traffic operation index according to the average traffic flow density;
(5) according to the traffic operation indexes of all road sections in the entrance direction of the intersection and the road grade attributes of the road sections, the traffic operation indexes of the intersection are calculated in an aggregation mode, the signal control period of the intersection is calculated through an intersection traffic operation index-signal period relation model, and then the signal period of each intersection is obtained;
(6) calculating a traffic operation index of a main line of the intersection according to the traffic operation index of each road section in the inlet direction of the intersection and the road grade attribute of the road section, and comprehensively calculating the average value of the traffic operation indexes of the intersection of the regional road network;
(7) calculating the green ratio of each intersection trunk line of the regional road network according to a trunk line priority principle based on a regional road network intersection traffic running index average value-green ratio model, and then calculating the time of each intersection trunk line signal control red light and green light;
(8) and sending real-time parameters of intersection signal control to the signal control lamp by using the signal issuing terminal equipment and calling a database interface service, and dynamically inducing traffic of each intersection of the regional road network through the signal control lamp.
2. The method and apparatus of claim 1, wherein the method comprises: the method comprises the steps of constructing a traffic operation index model based on a video, constructing a traffic operation index-signal period relation model, and constructing a regional road network intersection traffic operation index average value-split ratio model.
3. The video-based regional self-optimization signal control method and device according to claim 2, wherein the video-based traffic operation index model is constructed by 4 parts including a road section traffic operation index, an intersection trunk traffic operation index and an average value of regional road network traffic operation indexes;
(31) extracting traffic flow data and speed data of each lane of a video detection section, and respectively calculating an average traffic flow parameter and an average speed parameter of the detection section on the level of space dimension and time dimension;
average traffic flow parameterBy the formulaCalculating to obtain the data, wherein N is the lane where the vehicle is located, N is the total number of lanes of the road section, and q is the total number of lanes of the road sectionnA traffic flow for an nth lane; average velocity parameter vnBy passingIs calculated to obtain, wherein vnIs the speed of the n-th lane,average speed per granularity period;
(32) average traffic flow density parameterBy the formulaCalculating to obtain;
(33) road section traffic operation index RTPI passing formula
Calculating to obtain the values of x, y, z, p and m, wherein the values of x, y, z, p and m are road traffic jam feeling optimization parameters;
(34) intersection traffic operation index ITPI passing formula
ITPI＝RTPI1*ω1+RTPI2*ω2+,...,+RTPIj*ωjIs calculated to obtain, wherein ω1,ω2,...,ωjWeighting coefficients for each inlet direction;
(35) intersection main line traffic operation index IATPI passing bulletin
IATPI＝RTPI1*ω1+RTPI2*ω2+,...,+RTPIh*ωhIs calculated to obtain, wherein ω1,ω2,...,ωhIs the weighting coefficient of the intersection trunk section.
4. The method and apparatus of claim 2, wherein the method comprises: and constructing a traffic operation index-signal period relation model, wherein a signal period parameter C is T ITPI/10, and T is a preset signal period parameter.
5. The method and apparatus of claim 2, wherein the method comprises: the regional road network intersection traffic running index average value-split green ratio model is constructed, and the regional road network intersection traffic running index average value AITPImeanIntersection trunk line split parameterGreen time G of main line at intersectioni＝Ci×riRed light time R of main line of intersectioni＝Ci-Gi-Y，CiControlling the cycle time for the signal at the ith intersection, GiIs the ith intersection zoneGreen time, R, of road networkiThe red light time of the road network of the ith intersection area is shown, and the Y represents the yellow light time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510443091.0A CN105139670A (en) | 2015-07-23 | 2015-07-23 | Video-based regional self-optimizing signal control method and apparatus |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510443091.0A CN105139670A (en) | 2015-07-23 | 2015-07-23 | Video-based regional self-optimizing signal control method and apparatus |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105139670A true CN105139670A (en) | 2015-12-09 |
Family
ID=54724997
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510443091.0A Pending CN105139670A (en) | 2015-07-23 | 2015-07-23 | Video-based regional self-optimizing signal control method and apparatus |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105139670A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106781564A (en) * | 2016-12-28 | 2017-05-31 | 安徽科力信息产业有限责任公司 | Bus signal priority control method based on video detector |
| CN107705586A (en) * | 2016-08-08 | 2018-02-16 | 阿里巴巴集团控股有限公司 | The wagon flow control method and device of intersection |
| CN108074406A (en) * | 2016-11-16 | 2018-05-25 | 杭州海康威视数字技术股份有限公司 | A kind of signal control method and system |
| CN109410607A (en) * | 2018-03-22 | 2019-03-01 | 合肥革绿信息科技有限公司 | A kind of crossroad signal machine control method based on video |
| CN109410606A (en) * | 2018-03-22 | 2019-03-01 | 合肥革绿信息科技有限公司 | A kind of trunk roads synergistic signal machine control method based on video |
| WO2021142642A1 (en) * | 2020-01-15 | 2021-07-22 | Beijing Didi Infinity Technology And Development Co., Ltd. | Efficient network-wide signal coordination with multiple cycle lengths and trajectory data |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1921589A2 (en) * | 2006-11-10 | 2008-05-14 | Hitachi, Ltd. | Traffic information interpolation system |
| CN104408925A (en) * | 2014-12-17 | 2015-03-11 | 合肥革绿信息科技有限公司 | Array radar based intersection running state evaluation method |
| CN104484994A (en) * | 2014-12-17 | 2015-04-01 | 合肥革绿信息科技有限公司 | Urban road network traffic operation index evaluation method and device based on array radar |
| CN104575051A (en) * | 2015-01-14 | 2015-04-29 | 合肥革绿信息科技有限公司 | Viaduct ramp intelligent signal control method and device based on array radars |
-
2015
- 2015-07-23 CN CN201510443091.0A patent/CN105139670A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1921589A2 (en) * | 2006-11-10 | 2008-05-14 | Hitachi, Ltd. | Traffic information interpolation system |
| CN104408925A (en) * | 2014-12-17 | 2015-03-11 | 合肥革绿信息科技有限公司 | Array radar based intersection running state evaluation method |
| CN104484994A (en) * | 2014-12-17 | 2015-04-01 | 合肥革绿信息科技有限公司 | Urban road network traffic operation index evaluation method and device based on array radar |
| CN104575051A (en) * | 2015-01-14 | 2015-04-29 | 合肥革绿信息科技有限公司 | Viaduct ramp intelligent signal control method and device based on array radars |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107705586A (en) * | 2016-08-08 | 2018-02-16 | 阿里巴巴集团控股有限公司 | The wagon flow control method and device of intersection |
| CN107705586B (en) * | 2016-08-08 | 2022-05-10 | 阿里巴巴集团控股有限公司 | Traffic flow control method and device for road intersection |
| CN108074406A (en) * | 2016-11-16 | 2018-05-25 | 杭州海康威视数字技术股份有限公司 | A kind of signal control method and system |
| CN106781564A (en) * | 2016-12-28 | 2017-05-31 | 安徽科力信息产业有限责任公司 | Bus signal priority control method based on video detector |
| CN109410607A (en) * | 2018-03-22 | 2019-03-01 | 合肥革绿信息科技有限公司 | A kind of crossroad signal machine control method based on video |
| CN109410606A (en) * | 2018-03-22 | 2019-03-01 | 合肥革绿信息科技有限公司 | A kind of trunk roads synergistic signal machine control method based on video |
| CN109410606B (en) * | 2018-03-22 | 2021-05-04 | 合肥革绿信息科技有限公司 | Main road cooperative annunciator control method based on video |
| CN109410607B (en) * | 2018-03-22 | 2021-05-04 | 合肥革绿信息科技有限公司 | Cross intersection signal machine control method based on video |
| WO2021142642A1 (en) * | 2020-01-15 | 2021-07-22 | Beijing Didi Infinity Technology And Development Co., Ltd. | Efficient network-wide signal coordination with multiple cycle lengths and trajectory data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104575051B (en) | Viaduct ramp intelligent signal control method and device based on array radars | |
| CN105139670A (en) | Video-based regional self-optimizing signal control method and apparatus | |
| US11069233B1 (en) | Video-based main road cooperative signal machine control method | |
| CN104778834B (en) | Urban road traffic jam judging method based on vehicle GPS data | |
| CN106781499B (en) | Traffic network efficiency evaluation system | |
| CN104464295B (en) | A kind of overhead Entrance ramp intelligence restricted driving method based on video | |
| CN105070073A (en) | Geomagnetism-based region self-optimization signal control method and device | |
| CN104408925A (en) | Array radar based intersection running state evaluation method | |
| WO2020083399A1 (en) | Coordination trunk line planning method and configuration system based on traffic flow data | |
| Hiribarren et al. | Real time traffic states estimation on arterials based on trajectory data | |
| CN104575050B (en) | A kind of fast road ramp intellectual inducing method and device based on Floating Car | |
| US10699568B1 (en) | Video-based crossroad signal machine control method | |
| CN104484994A (en) | Urban road network traffic operation index evaluation method and device based on array radar | |
| CN101894461A (en) | Method for predicting travel time on urban ground level roads | |
| CN104966403A (en) | Trunk line self-optimizing signal control method and device based on terrestrial magnetism | |
| CN113077626B (en) | Lane selection guiding method, device, system, equipment and storage medium | |
| CN104464294B (en) | A kind of road section traffic volume method for evaluating state based on array radar | |
| CN105118310A (en) | Video-based single-point self-optimization signal control method and device | |
| KR20140028801A (en) | Prediction of urban congestion using its based data | |
| CN104966404A (en) | Single-point self-optimization signal control method and device based on array radars | |
| CN107622668A (en) | A kind of dynamic and visual intersection management system for monitoring based on RFID | |
| CN104575049B (en) | A kind of elevated ramp intellectual inducing method and device based on array radar | |
| CN109785627A (en) | A kind of crossroad access flux monitoring system | |
| CN105070074A (en) | Region self-optimization signal control method based on array radar and device | |
| CN105070075A (en) | Trunk line self-optimization signal control method based on array radar and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20151209 |
|
| WD01 | Invention patent application deemed withdrawn after publication |